FOREIGN-WHISPERS / video_to_text.py
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Initial Commit
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import argparse
from moviepy.editor import VideoFileClip
import whisper
import os
import re
def extract_audio(video_path, audio_dir='./audio'):
os.makedirs(audio_dir, exist_ok=True)
base_filename = os.path.splitext(os.path.basename(video_path))[0]
audio_filename = os.path.join(audio_dir, base_filename + '.wav')
video_clip = VideoFileClip(video_path)
video_clip.audio.write_audiofile(audio_filename)
video_clip.close()
return audio_filename
def transcribe_audio(audio_path, model_type='base', transcribed_dir='./transcribed'):
model = whisper.load_model(model_type)
result = model.transcribe(audio_path)
os.makedirs(transcribed_dir, exist_ok=True)
base_filename = os.path.splitext(os.path.basename(audio_path))[0]
transcribed_filename = os.path.join(transcribed_dir, base_filename + '.txt')
with open(transcribed_filename, 'w') as file:
for segment in result['segments']:
start = segment['start']
end = segment['end']
text = segment['text']
file.write(f"[{start:.2f}-{end:.2f}] {text}\n")
return transcribed_filename, result['text']
def merge_lines(file_path):
timestamp_pattern = re.compile(r'\[(\d+\.\d+)-(\d+\.\d+)\]')
with open(file_path, 'r') as file:
lines = file.readlines()
merged_lines = []
i = 0
while i < len(lines):
line = lines[i].strip()
match = timestamp_pattern.match(line)
if match:
start_time = float(match.group(1))
text = line[match.end():].strip()
if not (text.endswith('.') or text.endswith('?')):
if i + 1 < len(lines):
next_line = lines[i + 1].strip()
next_match = timestamp_pattern.match(next_line)
if next_match:
end_time = float(next_match.group(2))
next_text = next_line[next_match.end():].strip()
merged_text = text + ' ' + next_text
merged_line = f"[{start_time:.2f}-{end_time:.2f}] {merged_text}\n"
merged_lines.append(merged_line)
i += 1
else:
end_time = float(match.group(2))
merged_lines.append(f"[{start_time:.2f}-{end_time:.2f}] {text}\n")
i += 1
with open(file_path, 'w') as file:
file.writelines(merged_lines)
return file_path
def convert_video_to_text(video_file_path, model_type='base'):
audio_path = extract_audio(video_file_path)
transcribed_path, _ = transcribe_audio(audio_path, model_type)
merge_lines(transcribed_path)
return transcribed_path
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Transcribe audio from video")
parser.add_argument("video_file", help="Path to the video file")
parser.add_argument("--model", help="Size of the whisper model (e.g., tiny, base, small, medium, large, huge).", default="base")
args = parser.parse_args()
convert_video_to_text(args.video_file, args.model)